MatchPyramid. This convolution-based architecture views text matching as image recognition [1]}. The model first constructs a word-by-word matching matrix by computing pairwise word similarities. This matrix is processed by several convolutional layers to capture the interaction patterns between words, phrases and sentences. In the first layer, a square kernel of size \(k\)  extracts a feature map from the matching matrix, which is aggregated by max-pooling to fix the feature size. Repetitions of these layers produce higher-level features of a pre-defined size as the final embedding.
